Greener Than Thou: People who protect the environment are more cooperative, compete to be environmental, and benefit from reputation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Protecting the environment is a social dilemma: environmental protection benefits everyone but is individually costly. We propose that protecting the environment is similar to other types of cooperation, in that environmentalism functions as a signal of one’s willingness to cooperate with others. We test several novel predictions from this hypothesis. We used a mathematical model to show that environmentalism can indicate one’s valuation of others and thus one’s cooperative intent. We found support for this prediction in two online studies, and then conducted two laboratory studies to extend the idea that environmentalism signals one’s willingness to cooperate. Participants donated more to an environmental charity when donations were public than when anonymous, but they donated the most when competing to be chosen by an observer for a subsequent cooperative game. In other words, people competed to donate more to the environment. Bigger donors benefited, as they were subsequently chosen more often and received more cooperation from their partners. Partners benefited from choosing environmental donors: bigger donors cooperated more with subsequent partners, such that environmental donations were reliably informative about participants’ future cooperativeness. We compare multiple theories about why people behave environmentally (indirect reciprocity, signal of wealth, signal of cooperative intent), and find most support for our proposed theory of signaling cooperative intent. By understanding the function of environmental behaviour and stimulating competitive giving, we can increase people’s support for environmental and other charitable causes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it